CN117556589A - Intelligent calibration method and system for electric quantity of instrument - Google Patents

Intelligent calibration method and system for electric quantity of instrument Download PDF

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CN117556589A
CN117556589A CN202311305334.5A CN202311305334A CN117556589A CN 117556589 A CN117556589 A CN 117556589A CN 202311305334 A CN202311305334 A CN 202311305334A CN 117556589 A CN117556589 A CN 117556589A
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driving speed
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刘佳晨
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Jiangyin Feiyang Electronic Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
    • B60K35/00—Instruments specially adapted for vehicles; Arrangement of instruments in or on vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables

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  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention relates to the technical field of electric data processing, and provides an intelligent calibration method and system for electric quantity of an instrument, wherein the method comprises the following steps: acquiring power model information; starting and acquiring driving mileage data, highest driving speed, lowest driving speed and uniform driving speed after charging is completed, and collecting corresponding experience data and historical charge and discharge records; the method comprises the steps of determining a model correction factor by using power model information and power loss information, setting an electric energy conversion model by combining experience data and historical charge and discharge records, inputting driving mileage data, highest driving speed, lowest driving speed and uniform driving speed, outputting a residual electric quantity calibration result, correcting the residual electric quantity and displaying and outputting the residual electric quantity, solving the technical problem that the residual electric quantity of a vehicle instrument cannot be truly reflected by the residual electric quantity of a vehicle-mounted battery pack, and realizing the multi-dimensional vehicle instrument electric quantity calibration from driving speed, driving mileage and power loss so as to achieve the technical effect of reflecting the residual electric quantity of the vehicle-mounted battery pack of an automobile more accurately and more in real time.

Description

Intelligent calibration method and system for electric quantity of instrument
Technical Field
The invention relates to the technical field of electric data processing, in particular to an intelligent calibration method and system for electric quantity of an instrument.
Background
The electric quantity calibration of the instrument is used for various electric vehicles, including personal transportation vehicles, logistics distribution vehicles, public transportation vehicles and the like, and the electric quantity calibration refers to detection and calibration of parameters such as the electric quantity of a vehicle storage battery, the output voltage of a generator and the like so as to more accurately and more timely reflect the residual electric quantity of the vehicle-mounted battery pack of the automobile. The calibration can help the vehicle owners to better know the actual electric quantity of the storage battery, so that the driving route is planned, faults such as flameout of the vehicle are avoided, and the driving safety and the running stability are improved.
However, since the electric quantity display instrument of the electric vehicle reflects the electric quantity of the electric vehicle by detecting parameters such as voltage, current and the like of the battery pack. However, due to the unavoidable internal resistance, electric leakage and other problems in the battery charging and discharging process, meanwhile, some electric vehicles can perform virtual marks on the electric quantity of the electric vehicle when leaving the factory, namely, the actual electric quantity is lower than the electric quantity displayed by an electric quantity display instrument.
In addition, after the electric vehicle is continuously used, factors such as self-discharge, battery aging, temperature rise and the like generated in the battery pack can influence the performance and the electric quantity of the battery pack. The self-discharge means that after the electric vehicle stops using for a long time, chemical reaction inside the battery pack still continues, so that the potential difference gradually decreases, and the electric quantity gradually decreases. And an increase in temperature affects the power output and cycle life of the battery pack.
In summary, the technical problem in the prior art is that the electric quantity of the vehicle instrument cannot truly reflect the residual electric quantity of the vehicle-mounted battery pack.
Disclosure of Invention
The application aims to solve the technical problem that the vehicle instrument electric quantity in the prior art cannot truly reflect the residual electric quantity of the vehicle-mounted battery pack.
In view of the above problems, the present application provides an intelligent calibration method and system for electric quantity of an instrument.
In a first aspect of the disclosure, an intelligent calibration method for an electric quantity of a meter is provided, where the method is applied to an intelligent calibration system for an electric quantity of a meter, and the intelligent calibration system for an electric quantity of a meter is in communication connection with an electric vehicle meter module, and the method includes: acquiring power supply model information of a target electric vehicle, wherein the power supply model information comprises power supply type information and power supply capacity specification of a power supply module of the target electric vehicle; after the target electric vehicle finishes charging, starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle; based on the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle, correspondingly acquiring historical driving mileage data, historical highest driving speed, historical lowest driving speed and historical uniform driving speed within a limited time range; based on a data storage module in the target electric vehicle, outputting historical charge-discharge records of the power supply module within a limited time range, wherein time sequence information of the historical charge-discharge records corresponds to time sequence information of the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed and the historical uniform driving speed one by one; determining a model correction factor by using the power model information and the power loss information, and setting an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charge and discharge record and the model correction factor, wherein the electric energy conversion model is communicated with the electric vehicle instrument module; and inputting the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle into the electric energy conversion model, outputting a residual electric quantity calibration result, correcting the residual electric quantity of the target electric vehicle, and simultaneously displaying and outputting by utilizing the electric vehicle instrument module.
In another aspect of the disclosure, an intelligent calibration system for an electric quantity of an instrument is provided, wherein the system comprises: the power model information acquisition module is used for acquiring power model information of a target electric vehicle, wherein the power model information comprises power type information and power capacity specification of a power module of the target electric vehicle; the data acquisition module is used for starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle after the target electric vehicle is charged; the historical data acquisition module is used for correspondingly acquiring historical driving mileage data, historical highest driving speed, historical lowest driving speed and historical constant driving speed within a limited time range based on the current driving mileage data, highest driving speed, lowest driving speed and constant driving speed of the target electric vehicle; the historical charge-discharge record output module is used for outputting historical charge-discharge records of the power supply module within a limited time range based on the data storage module in the target electric vehicle, and the time sequence information of the historical charge-discharge records corresponds to the time sequence information of the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed and the historical uniform driving speed one by one; the electric energy conversion model setting module is used for determining a model correction factor by utilizing the power model information and the power loss information, and setting an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charge and discharge record and the model correction factor, wherein the electric energy conversion model is communicated with an electric vehicle instrument module; and the electric quantity correction module is used for inputting the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle into the electric energy conversion model, outputting a residual electric quantity correction result, correcting the residual electric quantity of the target electric vehicle and simultaneously utilizing the electric vehicle instrument module to display and output.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
because the power supply model information is acquired; starting and acquiring driving mileage data, highest driving speed, lowest driving speed and uniform driving speed after charging is completed, and collecting corresponding experience data and historical charge and discharge records; the model correction factors are determined by using the power model information and the power loss information, the electric energy conversion model is set based on the experience data, the historical charge and discharge records and the model correction factors, the driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed are input, the remaining electric quantity calibration result is output, the remaining electric quantity is corrected and displayed and output is simultaneously displayed, and the technical effect that the vehicle instrument electric quantity calibration is carried out in multiple dimensions from the driving speed, the driving mileage and the power loss is realized, so that the remaining electric quantity of the vehicle-mounted battery pack of the automobile is reflected more accurately and more in real time is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a method for intelligently calibrating electric quantity of an instrument according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a possible method for setting a model correction factor in an intelligent calibration method for electric quantity of an instrument according to an embodiment of the present application;
fig. 3 is a schematic diagram of a possible flow chart of obtaining an electric energy conversion model in an intelligent calibration method for electric quantity of an instrument according to an embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of an intelligent calibration system for electric quantity of an instrument according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a power model information acquisition module 100, a data acquisition module 200, a historical data acquisition module 300, a historical charge and discharge record output module 400, an electric energy conversion model setting module 500 and an electric quantity correction module 600.
Detailed Description
The embodiment of the application provides an intelligent calibration method and system for the electric quantity of an instrument, which solve the technical problem that the electric quantity of a vehicle instrument cannot truly reflect the residual electric quantity of a vehicle-mounted battery pack, and realize the multi-dimensional calibration of the electric quantity of the vehicle instrument from driving speed, driving mileage and power consumption so as to achieve the technical effect of reflecting the residual electric quantity of the vehicle-mounted battery pack of an automobile more accurately and in real time.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides an intelligent calibration method for electric quantity of an instrument, where the method is applied to an intelligent calibration system for electric quantity of an instrument, and the intelligent calibration system for electric quantity of an instrument is communicatively connected with an electric vehicle instrument module, and the method includes:
s10: acquiring power supply model information of a target electric vehicle, wherein the power supply model information comprises power supply type information and power supply capacity specification of a power supply module of the target electric vehicle;
step S10 further includes the steps of:
s11: the electric vehicle instrument module is arranged on the main control board and comprises an MCU, and a power module, a wireless communication driving module, an LED driving module, a gear function selection module, a radio frequency driving module and an audio driving module of a target electric vehicle which are electrically connected with the MCU.
Specifically, the intelligent electric quantity calibration system is in communication connection with an electric vehicle instrument module, the communication connection is simply through signal transmission interaction, communication is formed between the intelligent electric quantity calibration system and the electric vehicle instrument module, the electric vehicle instrument module is arranged on a main control board and comprises an MCU (Micro Controller Unit, a microcontroller), a power module, a wireless communication driving module, an LED driving module, a gear function selection module, a radio frequency driving module and an audio driving module of a target electric vehicle which are electrically connected with the MCU, and hardware support is provided for intelligent electric quantity calibration of the instrument;
The power type information comprises, but is not limited to, a lithium iron phosphate battery, a lithium iron manganese phosphate battery, a lithium cobalt oxide battery and a ternary lithium battery, the power capacity specification can be 48V12Ah, 48V20Ah, 60V20Ah and 72V32An, when the voltage is constant, the larger the current is, the stronger the power is, the higher the battery capacity is, the power type information of the target electric vehicle is obtained, and the power type information comprises the power type information and the power capacity specification of a power module of the target electric vehicle, so that a data basis is provided for subsequent analysis.
S20: after the target electric vehicle finishes charging, starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle;
s30: based on the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle, correspondingly acquiring historical driving mileage data, historical highest driving speed, historical lowest driving speed and historical uniform driving speed within a limited time range;
s40: based on a data storage module in the target electric vehicle, outputting historical charge-discharge records of the power supply module within a limited time range, wherein time sequence information of the historical charge-discharge records corresponds to time sequence information of the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed and the historical uniform driving speed one by one;
Specifically, under the condition that the target electric vehicle completes one-time charging and the battery is fully charged, starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle, wherein the uniform driving speed is the speed which is continuously and stably maintained for a long time after the vehicle is started, generally, after the vehicle is started, the electric vehicle starts to uniformly drive after the opening of an accelerator pedal Map and a torque value corresponding to the speed are certain values, the electric vehicle starts to time after starting to uniformly drive, for example, the driving duration is 1min, the duration of keeping for 30km/h is 35s, and the 30km/h can be set as the uniform driving speed, the limiting time range can be the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle are used as search contents, a search character is set, the historical driving mileage data, the historical highest driving speed, the lowest driving speed and the historical uniform driving speed in a limited time range are correspondingly acquired, and the historical driving mileage data can be corresponding to a plurality of driving mileage data after the battery is fully charged for one time in the past year;
And based on a data storage module in the target electric vehicle, comparing the limited time range, limiting and extracting historical charge-discharge records of the power supply module, wherein time sequence information of the historical charge-discharge records corresponds to time sequence information of historical driving mileage data, historical highest driving speed, historical lowest driving speed and historical uniform driving speed one by one, the historical charge-discharge records comprise a series of parameter indexes such as initial charge electric quantity, complete charge electric quantity, charge duration and the like, and historical moment points corresponding to the time sequence information can be accurately positioned to each ms to provide a data basis for subsequent data analysis.
S50: determining a model correction factor by using the power model information and the power loss information, and setting an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charge and discharge record and the model correction factor, wherein the electric energy conversion model is communicated with the electric vehicle instrument module;
as shown in fig. 2, step S50 includes the steps of:
s51: obtaining the number of N battery blocks of the target electric vehicle, wherein N is more than or equal to 6 and more than or equal to 1;
S52: according to the parallel connection and/or serial connection modes corresponding to the number of the N battery blocks, calculating to obtain the rated voltage and rated capacity of the power supply in the power supply capacity specification;
s53: performing energy storage calculation through the power supply capacity specification in the power supply model information to obtain power supply theoretical stored energy of the target electric vehicle, wherein the power supply theoretical stored energy=power supply rated voltage×power supply rated capacity;
s54: and taking the power loss information as a weight, carrying out weighted correction on the power theoretical stored energy, and setting a weighted correction result as a model correction factor.
Specifically, the model correction factor is determined by using the power model information and the power loss information, wherein the model correction factor comprises the steps of obtaining the number N of battery blocks of the target electric vehicle, and generally, the number N of battery blocks in a battery box of the electric vehicle meets that the number N of the battery blocks is more than or equal to 6 and more than or equal to 1; according to the parallel connection and/or serial connection modes corresponding to the number of the N battery blocks, calculating to obtain the power supply rated voltage and the power supply rated capacity in the power supply capacity specification, wherein an electric vehicle storage battery with the capacity of 20 ampere hours is exemplified, and is formed by connecting 4 storage batteries in series, the voltage of each storage battery is 12V, the capacity is 20AH,48 V=12V×4, and further the power supply rated voltage 48V and the power supply rated capacity 20AH of the electric vehicle storage battery are determined, namely the parallel connection and/or serial connection calculation is required to be compared;
Performing energy storage calculation according to the power capacity specification in the power model information to obtain power theoretical stored energy of the target electric vehicle, wherein the power theoretical stored energy=power rated voltage×power rated capacity, and generally, when the voltage is constant, the larger the current is, the stronger the power is, the higher the battery capacity is, and the power theoretical stored energy 960WH corresponding to the 48V20Ah battery is; the power theory corresponding to the 60V20Ah battery stores energy 1200WH; the power theory corresponding to the 72V32An battery stores energy 2304WH;
taking the power type information and the power capacity specification of the power supply module of the target electric vehicle as sampling limiting contents, determining a sample power supply module and a sample charging and discharging frequency set, a sample battery life cycle set and a sample battery charging cycle set of the sample power supply module in big data, and constructing a power loss evaluation expert system by taking the sample power supply module and the sample charging and discharging frequency set, the sample battery life cycle set and the sample battery charging cycle set of the sample power supply module as a knowledge base;
the power loss information is obtained by evaluating the combination of the charge and discharge times, the service life period of the battery and the charging period of the battery: and introducing the power loss information into the power loss evaluation expert system for combined evaluation to obtain the power loss information, normalizing the power loss information and the power theoretical stored energy, taking the power loss information as a weight value after the normalization, carrying out weighted correction on the power theoretical stored energy, setting a weighted correction result as a model correction factor, and setting the model correction factor against the power loss to more accurately and more timely reflect the residual electric quantity of the vehicle-mounted power supply.
Taking the power loss information as a weight, performing weighted correction on the power theoretical stored energy, and setting a weighted correction result as a model correction factor, wherein the step S54 comprises the following steps:
s541: determining a driving habit parameter interval of the target electric vehicle according to the historical driving data of the target electric vehicle, wherein the driving habit parameter interval correspondingly comprises a driving speed index, an acceleration index and a power control index, and further comprises,
s541-a: carrying out statistical analysis on the historical driving data according to a time sequence analysis method to obtain the power control index;
s541-b: calculating according to the mean value and standard deviation of the running speed index to obtain a running speed control index;
s541-c: calculating to obtain an acceleration control index according to the mean value and standard deviation of the acceleration index;
s542: and performing habitual correction on the power theoretical stored energy by taking the power loss information as a weight according to the power control index, the running speed control index and the acceleration control index corresponding to the driving habit parameter interval, and taking a result of the habitual correction as a model correction factor.
Specifically, historical driving data of the target electric vehicle is collected, wherein the historical driving data comprises driving mileage, driving speed, acceleration, power control and other information, and corresponding time sequence information can be from a sensor of the vehicle, GPS track data, driving behavior record of a driver and the like.
And carrying out statistical analysis on the historical driving data by using a time sequence analysis method to obtain a power control index, wherein the time sequence analysis method can comprise a sliding window, an ARIMA model, hurst index estimation and the like, a proper method can be selected according to the characteristics and the requirements of actual data, and the statistical characteristics such as the mean value, the variance and the like of the power control index can be found through the time sequence analysis, so that the driving habit of a driver is reflected.
Calculating a running speed control index according to the mean value and the standard deviation of the running speed index, wherein the running speed control index can reflect the control habit and the capability of a driver for speed, such as that the driver is prone to fast driving or cautious driving; the acceleration control index is calculated according to the mean value and standard deviation of the acceleration index, and the acceleration control index can reflect the control habit and the capability of a driver on acceleration, such as whether the driver tends to accelerate rapidly or smoothly.
After the driving habit parameter interval is determined, the power consumption information can be used as a weight value to carry out habitual correction on the power theoretical stored energy. The process of specific correction may include the steps of: matching the historical driving data with corresponding driving mileage indexes, driving speed indexes, acceleration indexes and power control indexes to obtain a historical data sequence of each index; statistical analysis, such as mean, variance, hurst index, etc., is performed on the historical data sequence for each index to learn the statistical characteristics of each index.
According to the statistical characteristics of each index, the weight of each index can be determined, for example, if the standard deviation of a certain index is larger, the change of the index is larger, and the corresponding weight can be smaller; conversely, if the average value of a certain index is larger, it indicates that the overall level of the index is higher, and the corresponding weight may be larger.
According to the weight of each index, the stored energy of the power supply theory can be subjected to weighted correction, the specific correction method can comprise weighted average, weighted product and the like, a proper method can be selected according to actual requirements, and the corrected result is used as a model correction factor. And obtaining a driving habit parameter interval of the target electric vehicle, and performing habitual correction on the power supply theoretical stored energy by utilizing the parameters to obtain a more accurate residual electric quantity calibration result.
The embodiment of the application further comprises:
s543: taking the power loss information as a weight value, and performing habitual correction on the power theoretical stored energy to obtain a model correction factor M CF ＝E/(E+E Loss of ) Wherein M is CF For characterising mouldsCorrection factor E is used for representing power supply theory stored energy, E Loss of For characterizing the power loss energy.
Specifically, the model correction factor MCF is determined, and the model correction factor MCF may be obtained by performing weighted correction on the power supply theoretical stored energy E and the power supply loss energy E, that is, mcf=e/(e+e loss), where E represents the power supply theoretical stored energy and E loss represents the power supply loss energy, so that the difference between the actual stored energy and the theoretical stored energy of the power supply can be better understood and evaluated, and the remaining capacity of the target electric vehicle can be more accurately calibrated.
As shown in fig. 3, step S50 further includes the steps of:
s55: in the historical charge-discharge records, taking charge information as a first extraction target, and acquiring historical charge records;
s56: in the history charge-discharge record, taking discharge information as a second extraction target to obtain a history discharge record;
s57: setting an electric vehicle charging model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charging record and the model correction factor;
s58: setting an electric vehicle discharge model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical discharge record and the model correction factor;
s59: and combining the electric vehicle charging model and the electric vehicle discharging model as parallel processing channels to obtain an electric energy conversion model.
Specifically, setting an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charge-discharge records and the model correction factors, wherein the electric energy conversion model comprises the steps of separating charge records and discharge records in the historical charge-discharge records, and taking charge information as a first extraction target in the historical charge-discharge records to obtain the historical charge records; in the historical charge-discharge records, taking discharge information as a second extraction target, and acquiring historical discharge records to realize separation of charge records and discharge records;
Respectively constructing an electric vehicle charging model and an electric vehicle discharging model, and setting the electric vehicle charging model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charging record and the model correction factor; setting an electric vehicle discharge model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical discharge record and the model correction factor;
combining the electric vehicle charging model and the electric vehicle discharging model as parallel processing channels: the method comprises the steps of firstly carrying out clock frequency adjustment on the electric vehicle charging model and the electric vehicle discharging model, enabling model input time nodes of the two models combined by parallel processing channels to be synchronous with model output time nodes, carrying out channel combination after synchronization to obtain an electric energy conversion model, and providing a model foundation for electric energy conversion analysis of the electric vehicle.
Step S57 includes the steps of:
s571: creating a Figure1 on the Matlab based on the historical charging record, the Figure1 being used to map a charging time probability distribution;
s572: creating Figure2 on Matlab based on a plurality of daily mileage in the historical mileage data, the Figure2 being used to map daily mileage probability distribution;
S573: creating a Figure3 on Matlab based on a plurality of historical daily highest travel speeds of the historical highest travel speeds, the Figure3 being used to map a daily highest travel speed probability distribution;
s574: creating a Figure4 on Matlab based on a plurality of historical daily minimum travel speeds of the historical minimum travel speeds, the Figure4 being used to map a daily minimum travel speed probability distribution;
s575: creating a Figure5 on the Matlab based on a plurality of historical daily uniform velocity speeds of the historical uniform velocity speeds, the Figure5 being used to draw a daily uniform velocity probability distribution;
s576: and modeling the charging behavior of the electric vehicle according to the Monte Carlo algorithm through the charging time probability distribution, the daily driving mileage probability distribution, the daily highest driving speed probability distribution, the lowest driving speed probability distribution, the daily uniform driving speed probability distribution and the model correction factor to obtain the electric vehicle charging model.
Specifically, setting an electric vehicle charging model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charging record and the model correction factor, wherein the setting of the electric vehicle charging model comprises the steps of creating a Figure1 on a Matlab based on the historical charging record and corresponding time sequence information, wherein the horizontal axis of the Figure1 is the time sequence information, the vertical axis of the Figure1 is the historical charging record, and the Figure1 is used for drawing charging time probability distribution;
Based on a plurality of daily driving mileage in the historical driving mileage data and corresponding time sequence information, building Figure2 on Matlab, wherein the horizontal axis of Figure2 is the time sequence information, the vertical axis of Figure2 is a plurality of daily driving mileage, and Figure2 is used for drawing daily driving mileage probability distribution;
creating Figure3 on Matlab based on a plurality of historical daily highest running speeds and corresponding time sequence information, wherein the horizontal axis of the Figure3 is time sequence information, the vertical axis of the Figure3 is a plurality of historical daily highest running speeds, and the Figure3 is used for drawing daily highest running speed probability distribution, generally, in the driving process, the data sampling frequency of the historical daily highest running speeds is per minute/time, namely, the driving duration of the day is 30min28s, the obtained daily highest running speeds are 31 numerical values, and the numerical value is obtained by upward rounding;
creating Figure4 on Matlab based on the plurality of historical daily minimum travel speeds and the corresponding time sequence information, wherein the horizontal axis of the Figure4 is the time sequence information, the vertical axis of the Figure4 is the plurality of historical daily minimum travel speeds, and the Figure4 is used for drawing daily minimum travel speed probability distribution;
based on a plurality of historical daily constant speed running speeds and corresponding time sequence information in the historical constant speed running speeds, building a Figure5 on Matlab, wherein the transverse axis of the Figure5 is time sequence information, the longitudinal axis of the Figure5 is a plurality of historical daily constant speed running speeds, and the Figure5 is used for drawing a daily constant speed running speed probability distribution, in short, the historical daily minimum running speed, the historical daily maximum running speed and the historical daily constant speed running speed are respectively used for representing a running speed maximum value, a running speed minimum value and a continuously stable kept speed in the same time period, for example, the running duration is 1min, the highest speed in 1min is 10km/h, the highest speed in 1min is 50km/h, the duration of keeping 30km/h is 33s, and the 10km/h can be set as the historical daily minimum running speed, the 50km/h is set as the historical daily maximum running speed, and the 30km/h is set as the historical daily constant speed;
And according to a Monte Carlo algorithm, calling Figure1 corresponding to the charging time probability distribution, figure2 corresponding to the daily driving mileage probability distribution, figure3 corresponding to the daily highest driving speed probability distribution, figure4 corresponding to the lowest driving speed probability distribution, figure5 corresponding to the daily uniform driving speed probability distribution and the model correction factor, and modeling the charging behavior of the electric vehicle to obtain the electric vehicle charging model, thereby providing a model foundation for carrying out electric vehicle charging analysis.
Step S58 includes the steps of:
s581: creating a Figure6 on the Matlab based on the historical discharge record, the Figure6 being used to map the discharge time probability distribution;
s582: and modeling the electric vehicle discharge behavior according to the Monte Carlo algorithm through the discharge time probability distribution, the daily driving mileage probability distribution, the daily highest driving speed probability distribution, the lowest driving speed probability distribution, the daily uniform driving speed probability distribution and the model correction factor to obtain the electric vehicle discharge model.
Specifically, setting an electric vehicle discharge model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical discharge record and the model correction factor, wherein the setting of the electric vehicle discharge model comprises the steps of creating a Figure6 on a Matlab based on the historical discharge record and corresponding time sequence information, wherein the transverse axis of the Figure6 is the time sequence information, the vertical axis of the Figure6 is the historical discharge record, and the Figure6 is used for drawing discharge time probability distribution;
And calling the Figure6 corresponding to the discharge time probability distribution, the Figure2 corresponding to the daily driving mileage probability distribution, the Figure3 corresponding to the daily highest driving speed probability distribution, the Figure4 corresponding to the lowest driving speed probability distribution, the Figure5 corresponding to the daily uniform driving speed probability distribution and the model correction factor according to a Monte Carlo algorithm, and jointly modeling the electric vehicle discharge behavior to obtain an electric vehicle discharge model, thereby providing a model foundation for electric vehicle discharge analysis.
S60: and inputting the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle into the electric energy conversion model, outputting a residual electric quantity calibration result, correcting the residual electric quantity of the target electric vehicle, and simultaneously displaying and outputting by utilizing the electric vehicle instrument module.
Step S60 includes the steps of:
s61: taking the target electric vehicle position as a starting point, acquiring a charging pile position in a preset range, wherein the radius corresponding to the preset range is not more than 10km;
s62: setting a preset discharge threshold value when the number M of idle charging piles corresponding to the charging pile positions in the preset range meets M being more than or equal to 50;
S63: setting a preset discharge threshold lower limit which is the electric quantity required from the target electric vehicle position to the nearest idle charging pile position under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 50 and more than or equal to M & gt 5;
s64: under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 5 and more than or equal to 1, the upper limit of the preset discharging threshold is the electric quantity required by the target electric vehicle to traverse the M idle charging pile positions;
s65: and obtaining a preset discharge threshold interval through the preset discharge threshold upper limit and the preset discharge threshold lower limit, and starting the audio driving module to remind the target electric vehicle of nearby charging when the residual electric quantity of the target electric vehicle is corrected and falls in the preset discharge threshold interval.
Specifically, the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle are input into the electric energy conversion model, a remaining electric quantity calibration result is output, the remaining electric quantity calibration result is used for representing the actual sustainable use calibration information of the electric quantity corresponding to the remaining driving mileage, the remaining electric quantity calibration result and the remaining electric quantity of the target electric vehicle are subjected to normalization processing, the remaining electric quantity calibration result is used as a weight value after the normalization processing is used, the remaining electric quantity of the target electric vehicle is subjected to weighted correction, the weighted correction result is converted into a percentage form, and meanwhile, the electric vehicle meter module is used for display output;
After correcting the residual electric quantity of the target electric vehicle, the method further comprises the following steps: the method comprises the steps that an electric vehicle charging pile influences the range of a free activity area of an electric vehicle to a certain extent, if the electric vehicle runs to a small area of the charging pile and the electric vehicle is exhausted, a battery needs to be disassembled for charging, if the battery cannot be disassembled and can need trailer assistance, the electric vehicle is moved to an area where the charging pile exists, based on the electric vehicle charging pile, the position of the target electric vehicle is taken as a starting point, the position of the charging pile in a preset range is obtained, the radius corresponding to the preset range is not more than 10km, the position of the target electric vehicle is taken as a round point, and the inside of a circle defined by the radius corresponding to the preset range is the preset range;
the number M of the idle charging piles corresponding to the charging pile positions in the preset range meets M more than or equal to 50 (in this case, the aggregated idle charging piles are generally arranged, or a plurality of idle charging piles are arranged at the same position), the follow-up steps are not needed, namely, the charging piles in the current range are sufficient, the preset discharging threshold is directly set to 10%, namely, the target electric vehicle is reminded of nearby charging when the corrected electric quantity of the target electric vehicle is lower than 10%;
under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 50 and is more than or equal to M & gt 5 (in this case, scattered idle charging piles are generally arranged, namely 1-5 idle charging piles are arranged at the same position), namely the charging piles in the current range are sufficient, flexible selection of the target electric parking space can be supported, and a preset discharging threshold lower limit is set, wherein the preset discharging threshold lower limit is the electric quantity required from the target electric vehicle position to the nearest idle charging pile position;
Under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 5 and more than or equal to 1, namely the charging piles in the current range are relatively rare, flexible selection of the target electric parking space cannot be supported, and in the process of going to the selected idle charging piles, the risk of being occupied by other vehicles still exists, and the upper limit of the preset discharging threshold is the electric quantity required by the target electric vehicle to traverse the M idle charging pile positions;
the method comprises the steps of taking the upper limit of a preset discharge threshold value and the preset discharge threshold value as the upper limit of a preset discharge threshold value interval, taking the lower limit of the preset discharge threshold value and the preset discharge threshold value as the lower limit of the interval of the preset discharge threshold value interval, obtaining the preset discharge threshold value interval, enabling the audio driving module to remind the target electric vehicle of nearby charging after correcting the residual electric quantity of the target electric vehicle to fall in the preset discharge threshold value interval, and reasonably arranging a driving route and a charging plan to avoid the condition that the electric quantity is exhausted and cannot continue to run, so that the use experience of electric vehicle users can be greatly improved.
In summary, the method and the system for intelligently calibrating the electric quantity of the instrument provided by the embodiment of the application have the following technical effects:
1. Because the power supply model information is acquired; starting and acquiring driving mileage data, highest driving speed, lowest driving speed and uniform driving speed after charging is completed, and collecting corresponding experience data and historical charge and discharge records; the intelligent calibration method and the intelligent calibration system for the electric quantity of the instrument are used for realizing the multi-dimensional calibration for the electric quantity of the vehicle instrument from the driving speed, the driving mileage and the power loss, so as to achieve the technical effect of reflecting the residual electric quantity of the vehicle-mounted battery pack of the vehicle more accurately and more in real time.
2. Because the number of N battery blocks is obtained; according to the parallel connection and/or series connection modes, calculating to obtain the power supply rated voltage and the power supply rated capacity in the power supply capacity specification, and performing energy storage calculation to obtain the power supply theoretical stored energy; the power loss information is used as a weight, the power theory is subjected to weighted correction to store energy, the weighted correction result is set as a model correction factor, and the model correction factor is set against the power loss so as to accurately and more timely reflect the residual electric quantity of the vehicle-mounted power supply of the automobile.
Example two
Based on the same inventive concept as the method for intelligent calibration of the electric quantity of the meter in the foregoing embodiment, as shown in fig. 4, an embodiment of the present application provides an intelligent calibration system for the electric quantity of the meter, where the system includes:
the power model information acquisition module 100 is configured to acquire power model information of a target electric vehicle, where the power model information includes power type information and power capacity specification of a power module of the target electric vehicle;
the data acquisition module 200 is used for starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle after the target electric vehicle is charged;
the historical data acquisition module 300 is configured to correspondingly acquire historical driving mileage data, a historical highest driving speed, a historical lowest driving speed and a historical constant driving speed within a defined time range based on current driving mileage data, a highest driving speed, a lowest driving speed and a constant driving speed of the target electric vehicle;
the historical charge-discharge record output module 400 is configured to output, based on a data storage module in the target electric vehicle, a historical charge-discharge record of the power supply module within a defined time range, where time sequence information of the historical charge-discharge record corresponds to time sequence information of the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed and the historical uniform driving speed one by one;
The electric energy conversion model setting module 500 is configured to determine a model correction factor by using the power model information and the power loss information, and set an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical constant driving speed, the historical charge-discharge record and the model correction factor, wherein the electric energy conversion model is communicated with an electric vehicle instrument module;
and the electric quantity correction module 600 is used for inputting the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle into the electric energy conversion model, outputting a residual electric quantity correction result, correcting the residual electric quantity of the target electric vehicle and simultaneously utilizing the electric vehicle instrument module to display and output.
Further, the system includes:
the electric vehicle instrument module determining module is used for the electric vehicle instrument module is arranged on the main control board and comprises an MCU, and a power module, a wireless communication driving module, an LED driving module, a gear function selecting module, a radio frequency driving module and an audio driving module of a target electric vehicle which are electrically connected with the MCU.
Further, the system includes:
the battery block number acquisition module is used for acquiring the number of N battery blocks of the target electric vehicle, wherein N is more than or equal to 6 and more than or equal to 1;
the power supply rated index calculation module is used for calculating power supply rated voltage and power supply rated capacity in the power supply capacity specification according to the parallel connection and/or serial connection modes corresponding to the number of the N battery blocks;
the power supply theoretical stored energy calculating module is used for carrying out energy storage calculation according to the power supply capacity specification in the power supply model information to obtain the power supply theoretical stored energy of the target electric vehicle, wherein the power supply theoretical stored energy = power supply rated voltage x power supply rated capacity;
and the weighting correction module is used for taking the power loss information as a weight value, carrying out weighting correction on the power theoretical stored energy, and setting a result of the weighting correction as a model correction factor.
Further, the system includes:
a driving habit parameter interval determining module, configured to determine a driving habit parameter interval of the target electric vehicle according to historical driving data of the target electric vehicle, where the driving habit parameter interval correspondingly includes a driving speed index, an acceleration index, a power control index, and further includes,
The statistical analysis module is used for carrying out statistical analysis on the historical driving data according to a time sequence analysis method to acquire the power control index;
the running speed control index calculation module is used for calculating and obtaining a running speed control index according to the mean value and standard deviation of the running speed index;
the acceleration control index calculation module is used for calculating and obtaining an acceleration control index according to the mean value and the standard deviation of the acceleration index;
and the habitual correction module is used for carrying out habitual correction on the power theoretical stored energy by taking the power loss information as a weight value according to the power control index, the running speed control index and the acceleration control index corresponding to the driving habit parameter interval, and setting a result of the habitual correction as a model correction factor.
Further, the system includes:
the model correction factor calculation module is used for taking the power loss information as a weight value, and habitually correcting the power theoretical stored energy to obtain a model correction factor, M CF ＝E/(E+E Loss of ) Wherein M is CF For characterizing model correction factors, E for characterizing power supply theory stored energy, E Loss of For characterizing the power loss energy.
Further, the system includes:
the historical charging record acquisition module is used for taking charging information as a first extraction target in the historical charging and discharging records to acquire historical charging records;
the historical discharge record acquisition module is used for taking the discharge information as a second extraction target in the historical charge and discharge record to acquire a historical discharge record;
the electric vehicle charging model setting module is used for setting an electric vehicle charging model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charging record and the model correction factor;
the electric vehicle discharge model acquisition module is used for setting an electric vehicle discharge model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical discharge record and the model correction factor;
and the parallel processing channel merging module is used for merging the electric vehicle charging model and the electric vehicle discharging model as parallel processing channels to obtain an electric energy conversion model.
Further, the system includes:
the charging time probability distribution drawing module is used for creating Figure1 on the Matlab based on the historical charging record, wherein Figure1 is used for drawing the charging time probability distribution;
the daily driving mileage probability distribution drawing module is used for creating Figure2 on Matlab based on a plurality of daily driving mileage in the historical driving mileage data, wherein Figure2 is used for drawing daily driving mileage probability distribution;
the system comprises a daily highest running speed probability distribution drawing module, a daily highest running speed probability distribution drawing module and a daily highest running speed probability distribution drawing module, wherein the daily highest running speed probability distribution drawing module is used for creating Figure3 on Matlab based on a plurality of historical daily highest running speeds in the historical highest running speeds;
the system comprises a drawing day minimum driving speed probability distribution module, a setting module and a setting module, wherein the drawing day minimum driving speed probability distribution module is used for creating Figure4 on Matlab based on a plurality of historical day minimum driving speeds in the historical minimum driving speeds, and the Figure4 is used for drawing day minimum driving speed probability distribution;
the daily constant-speed running speed probability distribution drawing module is used for creating Figure5 on the Matlab based on a plurality of historical daily constant-speed running speeds in the historical constant-speed running speeds, wherein Figure5 is used for drawing daily constant-speed probability distribution;
The electric vehicle charging behavior modeling module is used for modeling the electric vehicle charging behavior according to a Monte Carlo algorithm through the charging time probability distribution, the daily driving mileage probability distribution, the daily highest driving speed probability distribution, the lowest driving speed probability distribution, the daily uniform driving speed probability distribution and the model correction factor to obtain the electric vehicle charging model.
Further, the system includes:
the discharge time probability distribution drawing module is used for creating Figure6 on the Matlab based on the historical discharge record, wherein the Figure6 is used for drawing the discharge time probability distribution;
the electric vehicle discharge behavior modeling module is used for modeling the electric vehicle discharge behavior according to a Monte Carlo algorithm through the discharge time probability distribution, the daily driving mileage probability distribution, the daily highest driving speed probability distribution, the lowest driving speed probability distribution, the daily uniform driving speed probability distribution and the model correction factor to obtain the electric vehicle discharge model.
Further, the system includes:
the charging pile position acquisition module is used for acquiring the charging pile position in a preset range by taking the target electric vehicle position as a starting point, wherein the radius corresponding to the preset range is not more than 10km;
The preset discharge threshold setting module is used for setting a preset discharge threshold when the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 50;
the preset discharging threshold lower limit setting module is used for setting a preset discharging threshold lower limit which is the electric quantity required by the target electric vehicle position to the nearest idle charging pile position under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 50 and more than 5;
the preset discharge threshold upper limit setting module is used for setting the electric quantity required by the target electric vehicle to traverse the M idle charging pile positions under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 5 and more than or equal to 1;
the audio driving module starting module is used for obtaining a preset discharging threshold interval through the preset discharging threshold upper limit and the preset discharging threshold lower limit, and when the residual electric quantity of the target electric vehicle is corrected and falls in the preset discharging threshold interval, the audio driving module is started to remind the target electric vehicle of nearby charging.
Any of the steps of the methods described above may be stored as computer instructions or programs in a non-limiting computer memory and may be called by a non-limiting computer processor to identify any of the methods to implement embodiments of the present application, without unnecessary limitations.
Further, the first or second element may not only represent a sequential relationship, but may also represent a particular concept, and/or may be selected individually or in whole among a plurality of elements. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (8)

1. The utility model provides an intelligent calibration method of instrument electric quantity, its characterized in that, the method is applied to the intelligent calibration system of instrument electric quantity, the intelligent calibration system of instrument electric quantity is connected with electric motor car instrument module communication, the method includes:
acquiring power supply model information of a target electric vehicle, wherein the power supply model information comprises power supply type information and power supply capacity specification of a power supply module of the target electric vehicle; and
after the target electric vehicle finishes charging, starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle;
based on the current driving mileage data, the highest driving speed, the lowest driving speed and the uniform driving speed of the target electric vehicle, correspondingly acquiring historical driving mileage data, historical highest driving speed, historical lowest driving speed and historical uniform driving speed within a limited time range;
Based on a data storage module in the target electric vehicle, outputting historical charge-discharge records of the power supply module within a limited time range, wherein time sequence information of the historical charge-discharge records corresponds to time sequence information of the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed and the historical uniform driving speed one by one;
determining a model correction factor by using the power model information and the power loss information, and setting an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charge-discharge record and the model correction factor, wherein the electric energy conversion model is communicated with an electric vehicle instrument module which is arranged on a main control board and comprises an MCU, and a power module, a wireless communication driving module, an LED driving module, a gear function selection module, a radio frequency driving module and an audio driving module of a target electric vehicle which is electrically connected with the MCU;
inputting current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle into the electric energy conversion model, outputting a residual electric quantity calibration result, correcting the residual electric quantity of the target electric vehicle, and simultaneously displaying and outputting by using the electric vehicle instrument module, and acquiring a charging pile position in a preset range by taking the position of the target electric vehicle as a starting point, wherein the radius corresponding to the preset range is not more than 10km;
Setting a preset discharge threshold value when the number M of idle charging piles corresponding to the charging pile positions in the preset range meets M being more than or equal to 50;
setting a preset discharge threshold lower limit which is the electric quantity required from the target electric vehicle position to the nearest idle charging pile position under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 50 and more than or equal to M & gt 5;
under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 5 and more than or equal to 1, the upper limit of the preset discharging threshold is the electric quantity required by the target electric vehicle to traverse the M idle charging pile positions;
and obtaining a preset discharge threshold interval through the preset discharge threshold upper limit and the preset discharge threshold lower limit, and starting the audio driving module to remind the target electric vehicle of nearby charging when the residual electric quantity of the target electric vehicle is corrected and falls in the preset discharge threshold interval.
2. The method of claim 1, wherein the model correction factor is determined using the power model information, power loss information, the method comprising:
obtaining the number of N battery blocks of the target electric vehicle, wherein N is more than or equal to 6 and more than or equal to 1;
According to the parallel connection and/or serial connection modes corresponding to the number of the N battery blocks, calculating to obtain the rated voltage and rated capacity of the power supply in the power supply capacity specification;
performing energy storage calculation through the power supply capacity specification in the power supply model information to obtain power supply theoretical stored energy of the target electric vehicle, wherein the power supply theoretical stored energy=power supply rated voltage×power supply rated capacity;
and taking the power loss information as a weight, carrying out weighted correction on the power theoretical stored energy, and setting a weighted correction result as a model correction factor.
3. The method of claim 2, wherein the power loss information is used as a weight, the power theoretical stored energy is subjected to weighted correction, and a result of the weighted correction is set as a model correction factor, the method comprising:
determining a driving habit parameter interval of the target electric vehicle according to the historical driving data of the target electric vehicle, wherein the driving habit parameter interval correspondingly comprises a driving speed index, an acceleration index and a power control index, and further comprises,
carrying out statistical analysis on the historical driving data according to a time sequence analysis method to obtain the power control index;
Calculating according to the mean value and standard deviation of the running speed index to obtain a running speed control index;
calculating to obtain an acceleration control index according to the mean value and standard deviation of the acceleration index;
and performing habitual correction on the power theoretical stored energy by taking the power loss information as a weight according to the power control index, the running speed control index and the acceleration control index corresponding to the driving habit parameter interval, and taking a result of the habitual correction as a model correction factor.
4. The method of claim 3, wherein the model correction factor, M, is obtained by habitually correcting the power theory stored energy using the power loss information as a weight CF ＝E/(E+E Loss of ) Wherein M is CF For characterizing model correction factors, E for characterizing power supply theory stored energy, E Loss of For characterizing the power loss energy.
5. The method of claim 1, wherein the setting of the power conversion model is based on the historical driving range data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charge-discharge record, the model correction factor, the method comprising:
In the historical charge-discharge records, taking charge information as a first extraction target, and acquiring historical charge records;
in the history charge-discharge record, taking discharge information as a second extraction target to obtain a history discharge record;
setting an electric vehicle charging model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical charging record and the model correction factor;
setting an electric vehicle discharge model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, the historical discharge record and the model correction factor;
and combining the electric vehicle charging model and the electric vehicle discharging model as parallel processing channels to obtain an electric energy conversion model.
6. The method of claim 5, wherein the setting an electric vehicle charging model based on the historical driving range data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, and the historical charging record, the model correction factor, the method comprising:
Creating a Figure1 on the Matlab based on the historical charging record, the Figure1 being used to map a charging time probability distribution;
creating Fi guide 2 on Matlab based on a plurality of daily mileage in the historical mileage data, wherein the Fi guide 2 is used for drawing daily mileage probability distribution;
creating a Figure3 on Matlab based on a plurality of historical daily highest travel speeds of the historical highest travel speeds, the Figure3 being used to map a daily highest travel speed probability distribution;
creating a Figure4 on Matlab based on a plurality of historical daily minimum travel speeds of the historical minimum travel speeds, the Figure4 being used to map a daily minimum travel speed probability distribution;
creating a Figure5 on the Matlab based on a plurality of historical daily uniform velocity speeds of the historical uniform velocity speeds, the Figure5 being used to draw a daily uniform velocity probability distribution;
and modeling the charging behavior of the electric vehicle according to the Monte Carlo algorithm through the charging time probability distribution, the daily driving mileage probability distribution, the daily highest driving speed probability distribution, the lowest driving speed probability distribution, the daily uniform driving speed probability distribution and the model correction factor to obtain the electric vehicle charging model.
7. The method of claim 6, wherein the setting an electric vehicle discharge model based on the historical driving range data, the historical highest driving speed, the historical lowest driving speed, the historical uniform driving speed, and the historical discharge record, the model correction factor, the method comprising:
creating a Figure6 on the Matlab based on the historical discharge record, the Figure6 being used to map the discharge time probability distribution;
and modeling the electric vehicle discharge behavior according to the Monte Carlo algorithm through the discharge time probability distribution, the daily driving mileage probability distribution, the daily highest driving speed probability distribution, the lowest driving speed probability distribution, the daily uniform driving speed probability distribution and the model correction factor to obtain the electric vehicle discharge model.
8. An intelligent calibration system for meter electric quantity, which is used for implementing the intelligent calibration method for meter electric quantity according to any one of claims 1-7, comprising:
the power model information acquisition module is used for acquiring power model information of a target electric vehicle, wherein the power model information comprises power type information and power capacity specification of a power module of the target electric vehicle; and
The data acquisition module is used for starting the target electric vehicle and acquiring current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle after the target electric vehicle is charged;
the historical data acquisition module is used for correspondingly acquiring historical driving mileage data, historical highest driving speed, historical lowest driving speed and historical constant driving speed within a limited time range based on the current driving mileage data, highest driving speed, lowest driving speed and constant driving speed of the target electric vehicle;
the historical charge-discharge record output module is used for outputting historical charge-discharge records of the power supply module within a limited time range based on the data storage module in the target electric vehicle, and the time sequence information of the historical charge-discharge records corresponds to the time sequence information of the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed and the historical uniform driving speed one by one;
the electric energy conversion model setting module is used for determining a model correction factor by utilizing the power model information and the power loss information, and setting an electric energy conversion model based on the historical driving mileage data, the historical highest driving speed, the historical lowest driving speed, the historical constant driving speed, the historical charge and discharge records and the model correction factor, wherein the electric energy conversion model is communicated with the electric vehicle instrument module which is arranged on a main control board and comprises an MCU, and a power module, a wireless communication driving module, an LED driving module, a gear function selection module, a radio frequency driving module and an audio driving module of a target electric vehicle which is electrically connected with the MCU;
The electric quantity correction module is used for inputting current driving mileage data, highest driving speed, lowest driving speed and uniform driving speed of the target electric vehicle into the electric energy conversion model, outputting a residual electric quantity correction result, correcting the residual electric quantity of the target electric vehicle, and simultaneously displaying and outputting by using the electric vehicle instrument module, and acquiring a charging pile position in a preset range by taking the position of the target electric vehicle as a starting point, wherein the radius corresponding to the preset range is not more than 10km;
setting a preset discharge threshold value when the number M of idle charging piles corresponding to the charging pile positions in the preset range meets M being more than or equal to 50;
setting a preset discharge threshold lower limit which is the electric quantity required from the target electric vehicle position to the nearest idle charging pile position under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 50 and more than or equal to M & gt 5;
under the condition that the number M of idle charging piles corresponding to the charging pile positions in the preset range is more than or equal to 5 and more than or equal to 1, the upper limit of the preset discharging threshold is the electric quantity required by the target electric vehicle to traverse the M idle charging pile positions;
And obtaining a preset discharge threshold interval through the preset discharge threshold upper limit and the preset discharge threshold lower limit, and starting the audio driving module to remind the target electric vehicle of nearby charging when the residual electric quantity of the target electric vehicle is corrected and falls in the preset discharge threshold interval.
CN202311305334.5A 2024-01-04 2024-01-04 Intelligent calibration method and system for electric quantity of instrument Pending CN117556589A (en)

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